An injury risk function defines the probability of an injury as a function of certain measurable or known predictors. In this paper, wavelet analysis is employed for the non-parametric estimation of injury risk functions. After a brief introduction of the wavelet theory, the representation of density function by wavelet series is given. A procedure for the estimation of density function is described. The risk function estimation for right-censored data is investigated by introducing hazard rate function and its wavelet estimator. The use of the developed method is illustrated in a case study, where two sets of data are used: simulation data with known distribution and censoring information, and thoracic impact testing data, which are assumed to be right- censored. Comparisons are made between the wavelet-based approach and the empirical Kaplan-Meier non-parametric method.


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    Titel :

    Wavelet-based Non-parametric Estimation of Injury Risk Functions


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    SAE World Congress & Exhibition ; 2007



    Erscheinungsdatum :

    2007-04-16




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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